1. Field of the Invention
The invention relates to analysis of healthcare reimbursement claims for fraud and abuse, and more particularly to the use of consistency modeling methodologies to identify potentially fraudulent or abusive activity in healthcare, both by providers and patients.
2. Description of the Related Art
Healthcare fraud continues to be a growing problem in the United States and abroad. According to the Centers for Medicare and Medicaid Services (CMS), fraud schemes range from those perpetrated by individuals acting alone to broad-based activities by institutions or groups of individuals, sometimes employing sophisticated telemarketing and other promotional techniques to lure consumers into serving as the unwitting tools in the schemes. Seldom do perpetrators target only one insurer or either the public or private sector exclusively. Rather, most are found to be simultaneously defrauding public sector victims such as Medicare and private sector victims simultaneously.
According to the Center for Medicare and Medicaid Services (CMS), annual healthcare expenditures in the United States totaled over $1.4 trillion dollars in 2001, and are expected to increase 6.5% a year. Though the amount lost to healthcare fraud and abuse cannot be quantified to the dollar, the general consensus is that a significant percentage is paid to fraudulent or abusive claims. A July 1997 audit of annual Medicare payments by the Inspector General found that approximately 14 percent of Medicare payments (about $23.2 billion) made in fiscal year 1996 were improperly paid, due to fraud, abuse, and the lack of medical documentation to support claims. Many private insurers estimate the proportion of healthcare dollars lost to fraud to be in the range of 3-5%, which amounts to roughly $30-$50 billion annually. It is widely accepted that losses due to fraud and abuse are an enormous drain on both the public and private healthcare systems.
In Medicare, the most common forms of provider fraud include:                Billing for services not furnished        Misrepresenting the diagnosis to justify payment        Soliciting, offering, or receiving a kickback        Unbundling or “exploding” charges        Falsifying certificates of medical necessity, plans of treatment, and medical records to justify payment        Billing for a service not furnished as billed; i.e., upcoding.        
In addition to provider fraud, there is also client abuse, arising from such activities as card-sharing, acting in collusion with a provider for kickbacks, etc.
One of the ways in which fraud can be evaluated in the medical care setting, and indeed many other transactional environments, is by modeling interactions between different entities such as individuals, organizations or groups. In such cases, the activity related to the problem at hand is largely described by a body of transaction data (historical and/or ongoing) that captures the behaviors, including interactions, of the relevant entities. A few sample settings along with the corresponding transaction data and related entities are described below in Table 1.
TABLE 1Problem/SettingTransactionsEntitiesHealthcare fraud and abuseClaims (inpatient and outpatient)Client (Patient), Doctor, Hospital,detectionPharmacy, LabCredit Card fraud detectionPurchases, Payments, Non-Account holder, Merchant, Creditmonetary transactionsCard issuerBank Checking SystemCheck processing transactionsAccount holder, Bank, TellerFood Stamp fraud detectionFood Stamp transactionsRetailer, Client (Recipient)Worker's Compensation fraudInjury report, Payments, MedicalClaimant, ProviderdetectionServices
In each of these settings, the common phenomenon is the fact that the encounters between the different entities are captured in the form of the associated transactions.
An entity is an operational unit within a given setting, application or environment and represents objects that interact within that setting, application or environment. The members of an entity are generally objects of a similar type. Different entities interact with each other and their interactions are encapsulated in the transaction data corresponding to that application. Thus, examples of entities in a healthcare setting are clients, providers (this includes doctors, hospitals, pharmacies, etc.), clients' families, etc., and their interactions are captured in the claims data; i.e. the interaction of a healthcare provider and a patient is captured in a claim by the provider for reimbursement. In the credit card environment, the interacting entities are account holders, merchants, credit card issuers, and the like and their interactions are captured through different types of transactions such as purchases and payments.
Usually, entities correspond to individuals or organizations that are part of the setting, as the examples above illustrate. However, more abstract entities characterizing a transaction may also be defined. Examples include procedure codes (describing the type of healthcare service rendered), resource utilization groups (RUG's), diagnosis-related groups (DRG's), and SIC codes (Standard Industry Codes), etc.
A member of an entity is an individual instance of the entity. For example, a specific doctor is a member of the healthcare provider entity; a particular grocery store is a member of the credit card merchant entity; and so on.
As noted above, a transaction captures information associated with an interaction between a group of entities. A transaction may initially arise between two entities (e.g. a doctor and a patient) and then be processed by still other entities (e.g. a pharmacy providing a prescription and a laboratory providing a lab test required by the doctor). Different types of transactions will typically capture different types of interactions or interactions between different groups of entities. For example in the credit card setting, a purchase transaction captures the interaction between the cardholder and the merchant, while a payment transaction encapsulates the information regarding the payments made by a cardholder to the credit card issuer. Similarly, in healthcare, an outpatient claim represents the service received by a client (i.e. patient) from a provider as part of an office or home visit, while an inpatient claim encodes data regarding a patient's stay at a hospital or another facility.
In the past, profiles have been created for individual entities and used to develop statistical models based solely on the profiles of the individual entities. For example, U.S. Pat. No. 5,819,226 discloses, among other things, the use of profiles of individual credit card account holders for modeling credit card fraud by such individuals. While this approach is useful for particular applications, in other applications it is desirable to understand the complex interactions between different entities. For example, in order to determine whether there is fraudulent activity by a healthcare provider, it is important to view the provider's activity not just in a vacuum, but also in relation to the activities of all other similar providers (e.g. providers having the same specialty), as well as in the context of the patients on whom the services are performed. Accordingly, profiles based only on transactions of individual members of the entity are insufficient to capture these rich interactions between entities in a manner that yields statistically useful information for modeling the interactions between entities.
Because of the high cost of healthcare fraud, profiling provider activity on the basis of medical claims sent for reimbursement in order to detect fraud and abuse is of significant importance to healthcare payers. The provider can generate extra income by, for example, exaggerating service performed, delivering unnecessary services, submitting claims for services never rendered, etc.
Accordingly, there is a need for a system and method to detect fraud in the healthcare industry by evaluating the legitimacy of medical claims.